Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: The purpose of this project is to construct a predictive model using various machine learning algorithms and to document the end-to-end steps using a template. The Fashion MNIST dataset is a multi-class classification situation where we are trying to predict one of several (more than two) possible outcomes.

INTRODUCTION: Fashion-MNIST is an MNIST-like fashion product dataset from Zalando Research. It consists of a training set of 60,000 clothing article examples and a test set of 10,000 examples. Each example is a 28×28 grayscale image, associated with a label from 10 classes. Zalando intends Fashion-MNIST to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms. It shares the same image size and structure of training and testing splits.

For this iteration, we will construct a regular dense, deep learning model to classify the images. This model will serve as the baseline for future iterations.

ANALYSIS: The baseline performance of the model achieved an accuracy score of 88.58% after training for three epochs. The same baseline model processed the test dataset with an accuracy of 87.72%. If we were to train the model much further than three epochs, we could run into the over-fitting issue.

CONCLUSION: For this dataset, the model built using Keras and TensorFlow achieved a satisfactory result and should be considered for future modeling activities.

Dataset Used: Fashion-MNIST Dataset

Dataset ML Model: Multi-class classification with numerical attributes

Dataset Reference: https://github.com/zalandoresearch/fashion-mnist

One potential source of performance benchmarks: https://www.kaggle.com/zalando-research/fashionmnist

The HTML formatted report can be found here on GitHub.